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Wahl et al. BMC Medicine (2015) 13:48 DOI 10.1186/s12916-015-0282-yRESEARCH ARTICLEOpen AccessMulti-omic signature of body weight change: results from a population-based cohort studySimone Wahl1,2,3*, Susanne Vogt1, Ferdinand St kler4, Jan Krumsiek4, J g Bartel4, Tim Kacprowski5, Katharina Schramm6,7, Maren Carstensen8,9, Wolfgang Rathmann10, Michael Roden8,9,11, Carolin Jourdan1, Antti J Kangas12, Pasi Soininen12,13, PubMed ID:https://www.ncbi.nlm.nih.gov/pubmed/28461567 Mika Ala-Korpela12,13,14,15, Ute N hlings16, Heiner Boeing17, Fabian J Theis4,18, Christa Meisinger1, Melanie Waldenberger1,2, Karsten Suhre19,20, Georg Homuth5, Christian Gieger1,2, Gabi Kastenm ler19, Thomas Illig1,2,21, Jakob Linseisen1, Annette Peters1,3,22, Holger Prokisch6,7, Christian Herder8,9, Barbara Thorand1,3 and Harald Grallert1,2,AbstractBackground: Excess body weight is a major risk factor for cardiometabolic diseases. The complex molecular mechanisms of body weight change-induced metabolic perturbations are not fully understood. Specifically, in-depth molecular characterization of long-term body weight change in the general population is lacking. Here, we pursued a multi-omic approach to comprehensively study metabolic consequences of body weight change during a seven-year follow-up in a large prospective study. Methods: We used data from PubMed ID:http://www.ncbi.nlm.nih.gov/pubmed/27689333 the population-based Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 cohort. At follow-up (F4), two-platform serum metabolomics and whole blood gene expression measurements were obtained for 1,631 and 689 participants, respectively. Using weighted correlation network analysis, omics data were clustered into Dalfopristin cost modules of closely connected molecules, followed by the formation of a partial correlation network from the modules. Association of the omics modules with previous annual percentage weight change was then determined using linear models. In addition, we performed pathway enrichment analyses, stability analyses, and assessed the relation of the omics modules with clinical traits. Results: Four metabolite and two gene expression modules were significantly and stably associated with body weight change (P-values ranging from 1.9 ?10-4 to 1.2 ?10-24). The four metabolite modules covered major branches of metabolism, with VLDL, LDL and large HDL subclasses, triglycerides, branched-chain amino acids and markers of energy metabolism among the main representative molecules. One gene expression module suggests a role of weight change in red blood cell development. The other gene expression module largely overlaps with the lipid-leukocyte (LL) module previously reported to interact with serum metabolites, for which we identify additional co-expressed genes. The omics modules were interrelated and showed cross-sectional associations with clinical traits. Moreover, weight gain and weight loss showed largely opposing associations with the omics modules. Conclusions: Long-term weight change in the general population globally associates with serum metabolite concentrations. An integrated metabolomics and transcriptomics approach improved the understanding of molecular mechanisms underlying the association of weight gain with changes in lipid and amino.
